blocks|key|3318279|text|这取决于“相似”对你意味着什么。对于计算机来说，相似的颜色可能会被人类感知到非常不同的颜色。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3318280|OTOH，HSV模型可以很好地拟合，因为它对于非机器人来说非常直观。|3318281|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

It depends on what "similar" means to you. Colours similar for a computer can be very differently perceived by a human. 

OTOH, HSV model could fit rather nice, as it's pretty intuitive for non-robots.

blocks|key|1002088|text|我曾经使用Lab颜色空间和计算color+difference+(CIE76对我来说已经足够好了)获得了很好的结果。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1002089|来自the+Wikipedia+link|1002090|1002091|与RGB和CMYK颜色模型不同，Lab颜色设计为接近人类视觉。|blockquote|1002092|1002093|因此，如果你对相似的定义是：“他们看起来很相似”，我会选择Lab。|1002094|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Lab_color_space|1|http://en.wikipedia.org/wiki/Color_difference|2|http://en.wikipedia.org/wiki/Lab_color_space#Advantages^0|5|3|0|F|G|1|0|2|I|2|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@$A|Z|B|10|1|11]|$A|12|B|13|1|14]]|C|$]]|$1|D|3|E|5|6|7|15|8|@]|9|@$A|16|B|17|1|18]]|C|$]]|$1|F|3|-4|5|6|7|19|8|@]|9|@]|C|$]]|$1|G|3|H|5|I|7|1A|8|@]|9|@]|C|$]]|$1|J|3|-4|5|6|7|1B|8|@]|9|@]|C|$]]|$1|K|3|L|5|6|7|1C|8|@]|9|@]|C|$]]|$1|M|3|-4|5|6|7|1D|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]|U|$5|P|Q|R|C|$S|V]]|W|$5|P|Q|R|C|$S|X]]]]

I once got good results using the <a href="http://en.wikipedia.org/wiki/Lab_color_space" rel="nofollow">Lab</a> color space and computing the <a href="http://en.wikipedia.org/wiki/Color_difference" rel="nofollow">color difference</a> (CIE76 was good enough for me).

From <a href="http://en.wikipedia.org/wiki/Lab_color_space#Advantages" rel="nofollow">the Wikipedia link</a>:

<blockquote>
 <blockquote>
 <blockquote>
 Unlike the RGB and CMYK color models, Lab color is designed to approximate human vision.
 </blockquote>
 </blockquote>
</blockquote>

So if your definition of similar is: "they look similar", I'd go with Lab.

blocks|key|4786501|text|Lab是一个非常好的颜色空间，但我知道由于它的非线性和通用性，使用起来有点困难。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4786502|如果您对色调感兴趣，并且正在处理希望轻松消除阴影和反射的图像，则HSV是一个非常好的颜色空间。|4786503|如果你对接近人类的视觉感知感兴趣，YCrCb是一个非常好的(并且没有实验室那么复杂)的颜色空间。它考虑到对于人类，R，G和B成分产生更强和更弱的刺激。|4786504|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

Lab is a very good color space but I understand that it is a bit more difficult to use due to its nonlinearity and generality.

HSV is a very good color space if you are interested in the hue and are dealing with images where you want to easily eliminate shadows and reflections.

YCrCb is a very good (and not as sophisticated as Lab) color space if you are interested in approximating human visual perception. It takes into account that for humans, R, G, and B components produce stronger and weaker stimuli.

blocks|key|1096880|text|检查此线程Python+-+Find+similar+colors,+best+way|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1096881|答案是:http://www.compuphase.com/cmetric.htm|1096882|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/8863810/python-find-similar-colors-best-way|1|http://www.compuphase.com/cmetric.htm^0|5|12|0|0|4|11|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

check this thread <a href="https://stackoverflow.com/questions/8863810/python-find-similar-colors-best-way">Python - Find similar colors, best way</a>

and the answer is :<a href="http://www.compuphase.com/cmetric.htm" rel="nofollow noreferrer">http://www.compuphase.com/cmetric.htm</a>

blocks|key|4786539|text|可以使用颜色空间HSV(色调、饱和度、值或亮度)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4786540|在将图像转换为HSV后，您必须将通道饱和度和值设置为一个公用值，如100或任何其他强调颜色的值。通过这种转换，色调将是唯一有一些变化的通道，这应该足以显示出只有很小变化的相似颜色。|4786541|如果你想减少间隔，你可以在通道色调的直方图上生成你想要的间隔，并计算与其他直方图的差异。计算的结果将是颜色之间的距离。|4786542|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

You can use the color space HSV(Hue, Saturation, Value or Brightness) .

After convert your image to HSV you must set the channels Saturation and value with a common value like 100 or any other that stress the colors. With that transformation the Hue will be the only channel with some variation and this should be enough to show only the similar colors with little variations.

If you want reduce the intervals, you can generate on histogram of the channel Hue with the interval that you want, and calculate the difference with the other histograms. The result of that calculation will be the distance between the colors.

What color space I should use if I am trying to find all the pixels of similar color ? I tried using GrayScale and ran DFS at the point in the image where I want to find similar pixels that are connected to it but seems like it is not the correct way. Which color space should I use that will give me help me finding the pixels of similar color in a that are connected starting from some point in image. Initially I have RGB image.

Best color space for finding similar colors

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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如果我试图找到所有颜色相似的像素，我应该使用什么颜色空间？我尝试使用GrayScale，并在图像中想要找到与其连接的相似像素的位置运行DFS，但似乎这不是正确的方法。我应该使用哪种颜色空间来帮助我找到从图像中的某个点开始连接的颜色相似的像素。最初我有RGB图像。

问寻找相似颜色的最佳颜色空间
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